Answer-then-Edit: Reasoning Skeleton Editing for Anti-Distillation with Preserved Utility

Researchers propose Skeleton-Guided Reasoning Editing (SGRE), an Answer-then-Edit framework that modifies reasoning traces post-hoc to prevent unauthorized knowledge distillation of proprietary large language models (LLMs) while preserving answer accuracy and trace naturalness. The method, inspired by Cognitive Load Theory, extracts and coarsens reasoning skeletons to increase extraneous cognitive load on student models, achieving state-of-the-art anti-distillation performance across diverse LLMs without utility loss.

arXiv:2607.20440v1 Announce Type: new Abstract: Proprietary large language models LLMs entail substantial intellectual and financial investment, making them valuable intellectual property IP . However, even when deployed via black-box APIs, these models remain vulnerable to unauthorized knowledge distillation, which allows adversaries to cheaply extract and replicate model capabilities. To address this issue, anti-distillation AD has been proposed to generate defensive outputs that hinder distillation effectiveness, overcoming the limitation of watermarking-based approaches that rely on post-hoc verification. However, existing AD methods based on internal model perturbations struggle to balance anti-distillability and utility e.g., answer accuracy and naturalness of reasoning traces, with stronger defenses often causing significant utility loss. To fill this gap, we propose \textbf{\underline{S}}keleton-\textbf{\underline{G}}uided \textbf{\underline{R}}easoning \textbf{\underline{E}}diting SGRE , an \textit{Answer-then-Edit} framework that performs post-hoc trace modification for anti-distillation. In the answer stage, the teacher model first generates clean reasoning traces, preserving the original reasoning accuracy while enabling more flexible control over trace naturalness. In the editing stage, we draw inspiration from Cognitive Load Theory CLT and introduce a three-stage strategy consisting of reasoning skeleton extraction, skeleton graph coarsening, and skeleton verbalization. These operations jointly perturb reasoning structures and augment textual complexity to amplify extraneous load on student models, hindering their acquisition of underlying reasoning patterns. Extensive experiments across diverse LLMs demonstrate that SGRE achieves state-of-the-art performance in reducing distillation effectiveness, while maintaining lossless reasoning accuracy and superior trace naturalness.